AGRUPAMIENTO DE DATOS DE SERIES DE TIEMPO. ESTADO DEL ARTE

Time series clustering has been an important research field in the last decade, providing useful and effective information in diverse domain. As outcome of the great existing interest for part of the scientific community of data mining area, innumerable research works have arisen that propose new algorithms and methodologies to identify cluster in the data time series. To provide an overview, this paper surveys and summarizes works that investigated the data time series clustering in diverse applications field. The basic concepts of time series clustering are presented and the surveyed works are organized into three groups: temporal-proximity-based, model-based and representation-based. The application areas are summarized with a brief description of the used data. The characteristics and particularities of some works are discussed

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices